


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str       ,       __magic_name__	:				str       ,       __magic_name__	:				PreTrainedTokenizer       ,       __magic_name__	:				int       ,       __magic_name__	:				Optional[int] = None       ,       )  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						{}

		if train_file is not None:
				lowercase__	     =						[train_file]
		if eval_file is not None:
				lowercase__	     =						[eval_file]
		if test_file is not None:
				lowercase__	     =						[test_file]

		lowercase__	     =						datasets.load_dataset("""csv"""       ,       data_files=__magic_name__	)
		lowercase__	     =						list(ds[list(files.keys()	)[0]].features.keys()	)
		lowercase__	     =						features_name.pop(__magic_name__	)
		lowercase__	     =						list(set(ds[list(files.keys()	)[0]][label_name]	)	)
		lowercase__	     =						{label: i for i, label in enumerate(__magic_name__	)}
		lowercase__	     =						tokenizer.model_input_names
		lowercase__	     =						{}

		if len(__magic_name__	) == 1:
				for k in files.keys():
						lowercase__	     =						ds[k].map(
						    lambda __magic_name__	: tokenizer.batch_encode_plus(
						        example[features_name[0]]       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       padding="""max_length"""	)       ,       batched=__magic_name__       ,       )
		elif len(__magic_name__	) == 2:
				for k in files.keys():
						lowercase__	     =						ds[k].map(
						    lambda __magic_name__	: tokenizer.batch_encode_plus(
						        (example[features_name[0]], example[features_name[1]])       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       padding="""max_length"""       ,       )       ,       batched=__magic_name__       ,       )

		def gen_train():
				for ex in transformed_ds[datasets.Split.TRAIN]:
						lowercase__	     =						{k: v for k, v in ex.items() if k in input_names}
						lowercase__	     =						labelaid[ex[label_name]]
						yield (d, label)

		def gen_val():
				for ex in transformed_ds[datasets.Split.VALIDATION]:
						lowercase__	     =						{k: v for k, v in ex.items() if k in input_names}
						lowercase__	     =						labelaid[ex[label_name]]
						yield (d, label)

		def gen_test():
				for ex in transformed_ds[datasets.Split.TEST]:
						lowercase__	     =						{k: v for k, v in ex.items() if k in input_names}
						lowercase__	     =						labelaid[ex[label_name]]
						yield (d, label)

		lowercase__	     =						(
		    tf.data.Dataset.from_generator(
		        __magic_name__       ,       ({k: tf.intaa for k in input_names}, tf.intaa)       ,       ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))       ,       )
		    if datasets.Split.TRAIN in transformed_ds
		    else None
		)

		if train_ds is not None:
				lowercase__	     =						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

		lowercase__	     =						(
		    tf.data.Dataset.from_generator(
		        __magic_name__       ,       ({k: tf.intaa for k in input_names}, tf.intaa)       ,       ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))       ,       )
		    if datasets.Split.VALIDATION in transformed_ds
		    else None
		)

		if val_ds is not None:
				lowercase__	     =						val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

		lowercase__	     =						(
		    tf.data.Dataset.from_generator(
		        __magic_name__       ,       ({k: tf.intaa for k in input_names}, tf.intaa)       ,       ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))       ,       )
		    if datasets.Split.TEST in transformed_ds
		    else None
		)

		if test_ds is not None:
				lowercase__	     =						test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

		return train_ds, val_ds, test_ds, labelaid


A			:						Optional[Any]        =  logging.getLogger(__name__)



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					field(metadata={'''help''': '''Which column contains the label'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''The path of the training file'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''The path of the development file'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''The path of the test file'''}				)
					A__     =					field(
					    default=1_28					,    metadata={
					        '''help''': (
					            '''The maximum total input sequence length after tokenization. Sequences longer '''
					            '''than this will be truncated, sequences shorter will be padded.'''
					        )
					    }					,    )
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)






@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''Set this flag to use fast tokenization.'''}				)
					# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
					# or just modify its tokenizer_config.json.
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,    )






def 					UpperCamelCase   (      )  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
		lowercase__     ,      lowercase__     ,      lowercase__	     =						parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir	)
		    and os.listdir(training_args.output_dir	)
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
				    """ --overwrite_output_dir to overcome."""	)

		# Setup logging
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""       ,       datefmt="""%m/%d/%Y %H:%M:%S"""       ,       level=logging.INFO       ,       )
		logger.info(
		    f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1	)}, '''
		    f'''16-bits training: {training_args.fpaa}'''	)
		logger.info(f'''Training/evaluation parameters {training_args}'''	)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		lowercase__	     =						AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,       cache_dir=model_args.cache_dir       ,       )

		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						get_tfds(
		    train_file=data_args.train_file       ,       eval_file=data_args.dev_file       ,       test_file=data_args.test_file       ,       tokenizer=__magic_name__       ,       label_column_id=data_args.label_column_id       ,       max_seq_length=data_args.max_seq_length       ,       )

		lowercase__	     =						AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,       num_labels=len(__magic_name__	)       ,       labelaid=__magic_name__       ,       idalabel={id: label for label, id in labelaid.items()}       ,       finetuning_task="""text-classification"""       ,       cache_dir=model_args.cache_dir       ,       )

		with training_args.strategy.scope():
				lowercase__	     =						TFAutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path       ,       from_pt=bool(""".bin""" in model_args.model_name_or_path	)       ,       config=__magic_name__       ,       cache_dir=model_args.cache_dir       ,       )

		def compute_metrics(__magic_name__	:				EvalPrediction	) -> Dict:
				lowercase__	     =						np.argmax(p.predictions       ,       axis=1	)

				return {"acc": (preds == p.label_ids).mean()}

		# Initialize our Trainer
		lowercase__	     =						TFTrainer(
		    model=__magic_name__       ,       args=__magic_name__       ,       train_dataset=__magic_name__       ,       eval_dataset=__magic_name__       ,       compute_metrics=__magic_name__       ,       )

		# Training
		if training_args.do_train:
				trainer.train()
				trainer.save_model()
				tokenizer.save_pretrained(training_args.output_dir	)

		# Evaluation
		lowercase__	     =						{}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""	)
				lowercase__	     =						trainer.evaluate()
				lowercase__	     =						os.path.join(training_args.output_dir       ,       """eval_results.txt"""	)

				with open(__magic_name__       ,       """w"""	) as writer:
						logger.info("""***** Eval results *****"""	)

						for key, value in result.items():
								logger.info(f'''  {key} = {value}'''	)
								writer.write(f'''{key} = {value}\n'''	)

						results.update(__magic_name__	)

		return results


if __name__ == "__main__":
	main()



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase     :	Dict       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	int       			=  {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""trocr"""
  a__       			=						["""past_key_values"""]
  a__       			=						{
      """num_attention_heads""": """decoder_attention_heads""",
      """hidden_size""": """d_model""",
      """num_hidden_layers""": """decoder_layers""",
  }
  def __init__(					self     :					Dict     ,				UpperCamelCase__     :					Optional[Any]=5_0265     ,				UpperCamelCase__     :					int=1024     ,				UpperCamelCase__     :					int=12     ,				UpperCamelCase__     :					List[Any]=16     ,				UpperCamelCase__     :					Union[str, Any]=4096     ,				UpperCamelCase__     :					List[Any]="gelu"     ,				UpperCamelCase__     :					Optional[int]=512     ,				UpperCamelCase__     :					Optional[Any]=0.1     ,				UpperCamelCase__     :					int=0.0     ,				UpperCamelCase__     :					str=0.0     ,				UpperCamelCase__     :					Optional[int]=2     ,				UpperCamelCase__     :					List[str]=0.02     ,				UpperCamelCase__     :					Dict=0.0     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					str=1     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					List[str]=2     ,				**UpperCamelCase__     :					Tuple     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			vocab_size
    __magic_name__								=			d_model
    __magic_name__								=			decoder_layers
    __magic_name__								=			decoder_attention_heads
    __magic_name__								=			decoder_ffn_dim
    __magic_name__								=			activation_function
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			dropout
    __magic_name__								=			attention_dropout
    __magic_name__								=			activation_dropout
    __magic_name__								=			init_std
    __magic_name__								=			decoder_layerdrop
    __magic_name__								=			use_cache
    __magic_name__								=			scale_embedding
    __magic_name__								=			use_learned_position_embeddings
    __magic_name__								=			layernorm_embedding

    super().__init__(
        pad_token_id=UpperCamelCase__     ,				bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				decoder_start_token_id=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

